Meta-Heuristics Based Load Balancing Optimization in Cloud Environment on Underflow and Overflow Conditions

In cloud environment, the main challenge is load balancing as it requires distributing the load among many various virtual machines (VM) while avoiding underflow and overflow conditions. In this article, the question that which load-balancing (overflow or underflow) management will better improves the performance and quality of service has been answered with a number of experiments. For experiment purpose, scientific workflow DAG files has been used with one host configuration. Ant Colony Optimization (ACO) and BAT algorithm are used for checking underflow and overflow conditions respectively for VMs. In proposed work, initially the workflow is parsed by Predict Earliest Finish Time (PEFT) heuristic to generate initial seed for meta-heuristic algorithms which will optimize the VM in terms of makespan and cost of execution. Different workflows have been used with varying number of VMs from 2 to 20. The results shows that makespan analysis is approximately overlapped for different workflow tasks and shows no significant difference however, the cost analysis show a significant change for overflow and underflow identification with cost for overflow condition is reduced significantly.